Modern global cellular and non-cellular positioning technologies are based on generating large global databases containing information on cellular and non-cellular signals. The information may originate entirely or partially from users of these positioning technologies. This approach may also be referred to as “crowd-sourcing”.
The information provided by users may be in the form of “fingerprints”, which contain a location that is estimated based on, for example, received satellite signals of a global navigation satellite system (GNSS) and measurements taken from one or more radio interfaces for signals of a cellular and/or non-cellular terrestrial system. In the case of measurements on cellular signals, the results of the measurements may contain a global and/or local identification of the cellular network cells observed, their signal strengths and/or path losses and/or timing measurements like timing advance (TA) or round-trip time. For measurements on wireless local area network (WLAN) signals, as an example of signals of a non-cellular system, the results of the measurements may contain at least one of a basic service set identification (BSSID), like the medium access control (MAC) address of observed access points (APs), the service set identifier (SSID) of the access points, and the signal strengths of received signals. A received signal strength indication, RSSI, or physical reception level may be expressed in dBm units with a reference value of 1 mW, for example.
Such data may then be transferred to a server or cloud, where the data may be collected and where further models may be generated based on the data for positioning purposes. Such further models can be coverage area estimates, communication node positions and/or radio channel models, with base stations of cellular communication networks and access points of WLANs being exemplary communication nodes. In the end, these refined models, also known as radio maps (RM) may be used for estimating the position of mobile terminals.
Fingerprints do not necessarily have to comprise a GNSS based position. They may also include cellular and/or WLAN measurements only. In this case the fingerprint could be assigned a position for example based on a WLAN based positioning in a server. Such self-positioned fingerprints can be used to learn cellular network information, in case there are cellular measurements in the fingerprint. Moreover, in a set of WLAN measurements in a fingerprint there may be, in addition to measurements for known WLAN access points, also measurements for unknown access points and the position of the unknown access points can be learned through these self-positioned fingerprints. Finally, more data can be learned of previously known access points based on self-positioned fingerprints.
It may be noted that even when using a mobile terminal having GNSS-capabilities, a user may benefit from using cellular/non-cellular positioning technologies in terms of time-to-first-fix and power consumption. Also, not all applications require a GNSS-based position. Furthermore, cellular/non-cellular positioning technologies work indoors as well, which is generally a challenging environment for GNSS-based technologies.